Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sherwin Prince Daniel, R.; Bijeesh, T.V.; Shamanth, N. Title A name given to the resource Premium Unlocked AI for Medical Document Decoding Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Conference on NexGen Networks and Cybernetics, IC2NC 2025 - Proceedings;pp.774-780 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IC2NC67409.2025.11376438" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC2NC67409.2025.11376438</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035605413?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035605413?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sherwin Prince Daniel R., Christ University, Department of AI, Ml &amp; Data Science, Bangalore, India; Bijeesh T.V., Christ University, Department of AI, Ml &amp; Data Science, Bangalore, India; Shamanth N., Christ University, Department of Computer Science &amp; Engineering, Bangalore, India Description An account of the resource As healthcare systems evolve to become more digital, an enormous volume of medical data is available in various formats, including unstructured data, scanned documents, handwritten prescriptions, diagnostic images, audio transcriptions, and clinical video recordings. The complexity and unorganised form of data continue to pose serious challenges with regard to automation, accuracy, and consistency in healthcare and insurance businesses. This study introduces an AI-based multimodal framework that incorporates the use of Optical Character Recognition (OCR), the MiniCPMV-4.5 model, and Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) to enhance the intelligent processing and contextual comprehension of intricate medical data, thus overcoming these limitations. It applies OCR to scanned images and handwritten documents to precisely recover the textual information from them and uses domain-specific named entity recognition (NER) to recognize significant medical information, e.g., patient information, diagnoses, procedures, and financial information. The extracted information is then converted to vector embeddings and stored in a powerful vector database, Milvus, that enables fast and efficient semantic search as well as context-sensitive reasoning. The proposed framework, along with the visual and auditory inputs, video understanding, multilingual capacity, and the S2S (speech-to-speech) and TTS (text-to-speech) translation, makes it more accessible and engaging to the user. This system reduces the level of human involvement and provides real-time insights quickly and more precisely so that more efficient decisions and operations can be made in the fields of healthcare and insurance. 2025 IEEE. Subject The topic of the resource Artificial Intelligence; Deep Learning; Large Language Models (LLMs); Medical Document Analysis; Multilingual Processing; Named Entity Recognition (NER); Optical Character Recognition (OCR); Retrieval Augmented Generation (RAG); Speech-to-Text (STT); Text-to-Speech (TTS) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159484-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online